Systems and methods for customer value optimization involving relationship optimization

Abstract

Systems and methods can provide for customer value optimization. The customer value optimization can include analyzing certain transaction and/or non-transaction data of customers with one or more predictive models to determine predictive modeling scores, values, or indicators. These one or more predictive modeling scores, values, or indicators can be used with other transaction or non-transaction data of customers, either alone or with other derived values/calculations, to provide certain optimizations relating to relationship optimization.

1 - 24 . (canceled) 
     
     
         25 . A system, comprising:
 at least one network interface;   at least one memory storing computer-executable instructions; and   at least one processor communicatively coupled to the at least one network interface and the at least one memory, and configured to access the at least one memory and execute the computer-executable instructions to:
 receive, via at least a first network interface of the at least one network interface, and on behalf of a financial institution, i) an indication of one or more optimization objectives associated with one or more optimization processes and ii) an indication of one or more optimization constraints associated with the one or more optimization processes; 
 receive, via at least a second network interface of the at least one network interface, customer financial data associated with a customer of the financial institution, wherein the customer financial data comprises at least one of i) financial account data associated with the customer or ii) financial transaction data associated with the customer; 
 generate derived data associated with the customer based at least in part on the customer financial data, wherein the derived data comprises at least one of i) data indicative of a customer segment with which the customer is associated, ii) data indicative of one or more predictive model values generated based at least in part on one or more predictive models, or iii) data indicative of one or more computational values; 
 determine that the customer is eligible for at least one of the one or more optimization processes based at least in part on i) at least a portion of the derived data and ii) the one or more optimization constraints; 
 identify, based at least in part on the one or more optimization objectives, a particular optimization process to execute for the customer, wherein the at least one of the one or more optimization processes comprises the particular optimization process; 
 execute the particular optimization process to identify a recommended next action to be taken with respect to the customer; and 
 transmit, via at least a third network interface of the at least one network interface, information indicative of the recommended next action. 
   
     
     
         26 . The system of  claim 25 , wherein the particular optimization process comprises one of: i) a product/service origination optimization process, ii) a relationship optimization process, or iii) a revenue/cost improvement optimization process. 
     
     
         27 . The system of  claim 26 , wherein the particular optimization process comprises the product/service origination optimization process, wherein the derived data comprises the data indicative of one or more predictive model values generated based at least in part on one or more predictive models and the data indicative of one or more computational values, wherein the one or more predictive model values comprise a respective probability of purchase for each candidate product or service of a set of one or more candidate products or services, and wherein the at least one processor is configured to execute the product/service origination optimization process by executing the computer-executable instructions to:
 identify a subset of the set of one or more candidate products or services, wherein the identifying comprises determining that the respective probability of purchase associated with each candidate product or service of the subset meets or exceeds a threshold value; and   identify a particular candidate product or service of the subset based at least in part on a respective future product or service value associated with the particular candidate product or service and included in the one or more computational values,   wherein the recommended next action comprises an offering of the particular candidate product or service.   
     
     
         28 . The system of  claim 27 , wherein the at least one processor is further configured to execute the product/service optimization process by executing the computer-executable instructions to:
 generate a customized offering of the particular candidate product or service,   wherein the recommended next action comprises the customized offering of the particular candidate product or service.   
     
     
         29 . The system of  claim 27 , wherein the threshold value is a first threshold value, the subset is a first subset, and the at least one processor is further configured to execute the product/service origination optimization process by executing the computer-executable instructions to:
 determine a respective future product or service value associated with each of at least one candidate product or service of the first subset;   determine a future customer value associated with the customer based at least in part on the respective future product or service value associated with the each of the at least one candidate product or service of the first subset;   determine that the future customer value meets or exceeds a second threshold value; and   identify a second subset of one or more candidate products or services from the first subset, wherein the respective future product or service value associated with each candidate product or service of the second subset is greater than the respective future product or service value associated with each candidate product or service of the first subset that does not form part of the second subset,   wherein the particular candidate product or service is included in the second subset.   
     
     
         30 . The system of  claim 29 , wherein the at least one processor is further configured to execute the product/service origination optimization process by executing the computer-executable instructions to:
 identify a third subset of one or more candidate products or services from the second subset, wherein each candidate product or service in the third subset is not currently held by the customer;   identify a candidate product or service of the third subset that is associated with a greater respective future product or service value than each other candidate product or service in the third subset; and   select the identified candidate product or service of the third subset as the particular candidate product or service.   
     
     
         31 . The system of  claim 27 , wherein the threshold value is a first threshold value, and wherein at least one processor is configured to identify the particular candidate product or service of the subset by executing the computer-executable instructions to determine that the respective future product or service value associated with the particular candidate product or service meets or exceeds a second threshold value. 
     
     
         32 . The system of  claim 26 , wherein the particular optimization process comprises the relationship optimization process, and wherein the at least one processor is configured to execute the relationship optimization process by executing the computer-executable instructions to:
 determine a current customer value associated with the customer;   determine a future customer value associated with the customer;   determine an overall customer value associated with the customer based at least in part on a combination of the current customer value and the future customer value; and   determine a value level associated with the customer based at least in part on at least one of: (i) a comparison of the overall value associated with the customer to one or more overall value thresholds or (ii) a comparison of the future customer value to a first threshold and a comparison of the current customer value to a second threshold,   wherein the recommended next action is determined based at least in part on the value level associated with the customer.   
     
     
         33 . The system of  claim 32 , wherein the at least one processor is further configured to execute the relationship optimization process by executing the computer-executable instructions to:
 identify a respective set of one or more eligibility rules associated with each of one or more candidate recommended next actions;   analyze the respective set of one or more eligibility rules associated with each of at least one of the one or more candidate recommended next actions;   determine, based at least in part on the analyzing, that each eligibility rule in the respective set of one or more eligibility rules associated with a particular candidate recommended next action is satisfied; and   select the particular candidate recommended next action as the recommended next action.   
     
     
         34 . The system of  claim 33 , wherein the at least one processor is configured to identify the respective set of one or more eligibility rules associated with each of the one or more candidate recommended next actions based at least in part on at least one of: (i) the customer segment associated with the customer, (ii) the current customer value, (iii) the future customer value, (iv) the value level associated with the customer, or (v) the one or more optimization constraints. 
     
     
         35 . The system of  claim 26 , wherein the at least one processor is configured to determine the current customer value by executing the computer-executable instructions to:
 identify a set of one or more products or services currently held by the customer;   determine a respective set of one or more financial metrics associated with each product or service in the set of one or more products or services;   determine a respective current product or service value for each product or service in the set of one or more products or services based at least in part on the respective set of one or more financial metrics; and   determine the current customer value based at least in part on a combination of each respective current product or service value.   
     
     
         36 . The system of  claim 26 , wherein the particular optimization process is a revenue/cost improvement optimization process, wherein the derived data comprises the data indicative of a customer segment with which the customer is associated and the data indicative of one or more predictive model values, wherein the one or more predictive model values comprise an attrition risk associated with the customer, and wherein the at least one processor is configured to execute the revenue/cost improvement optimization process by executing the computer-executable instructions to:
 determine that the attrition risk meets or exceeds a threshold value;   determine a set of one or more candidate products or services based at least in part on the customer segment;   identify a subset of one or more candidate products or services from the set of one or more candidate products or services, wherein the one or more candidate products or services of the subset are not currently held by the customer;   identify a respective set of one or more eligibility rules associated with each candidate product or service in the subset;   analyze the respective set of one or more eligibility rules associated with each candidate product or service in the subset; and   determine, based at least in part on the analyzing, that each eligibility rule in the respective set of one or more eligibility rules associated with a particular candidate product or service is satisfied,   wherein the recommended next action is an offering of the particular candidate product or service.   
     
     
         37 . The system of  claim 25 , wherein the one or more optimization objectives comprise at least one of:
 i) identification of a candidate product or service to offer to the customer,   ii) determination as to whether the customer is eligible for a particular product or service, or   iii) determination of a set of one or more actions for improving a relationship between the customer and the financial institution.   
     
     
         38 . The system of  claim 25 , wherein the one or more optimization constraints comprise at least one of:
 i) restricting the recommended next action to an offering to the customer of a product or service included in a predetermined set of one or more products or services,   ii) limiting a cost of acquisition associated with the recommended next action to a first maximum threshold value,   iii) requiring a revenue increase or cost decrease associated with the recommended next action to meet or exceed a minimum threshold value,   iv) restricting targeting of the recommended next action to a predetermined set of one or more customer segments comprising the customer segment with which the customer is associated;   v) restricting transmission of information associated with the recommended next action to a predetermined set of one or more channels; or   vi) limiting a risk of default or delinquency to a second maximum threshold value.   
     
     
         39 . The system of  claim 25 , wherein at least one of the first network interface, the second network interface, or the third network interface are a same network interface. 
     
     
         40 . A method, comprising:
 receiving, by a computerized financial system comprising one or more computers and on behalf of a financial institution, i) an indication of one or more optimization objectives associated with one or more optimization processes and ii) an indication of one or more optimization constraints associated with the one or more optimization processes;   receiving, by the computerized financial system, customer financial data associated with a customer of the financial institution, wherein the customer financial data comprises at least one of i) financial account data associated with the customer or ii) financial transaction data associated with the customer;   generating, by the computerized financial system, derived data associated with the customer based at least in part on the customer financial data, wherein the derived data comprises at least one of i) data indicative of a customer segment with which the customer is associated, ii) data indicative of one or more predictive model values generated based at least in part on one or more predictive models, or iii) data indicative of one or more computational values;   determining, by the computerized financial system, that the customer is eligible for at least one of the one or more optimization processes based at least in part on i) at least a portion of the derived data and ii) the one or more optimization constraints;   identifying, by the computerized financial system and based at least in part on the one or more optimization objectives, a particular optimization process to execute for the customer, wherein the at least one of the one or more optimization processes comprises the particular optimization process;   executing, by the computerized financial system, the particular optimization process to identify a recommended next action to be taken with respect to the customer; and   transmitting, by the computerized financial system, information indicative of the recommended next action.   
     
     
         41 . The method of  claim 40 , wherein the particular optimization process comprises one of: i) a product/service origination optimization process, ii) a relationship optimization process, or iii) a revenue/cost improvement optimization process. 
     
     
         42 . The method of  claim 41 , wherein the particular optimization process comprises the product/service origination optimization process, wherein the derived data comprises the data indicative of one or more predictive model values generated based at least in part on one or more predictive models and the data indicative of one or more computational values, wherein the one or more predictive model values comprise a respective probability of purchase for each candidate product or service of a set of one or more candidate products or services, and wherein the product/service origination optimization process comprises:
 identifying, by the computerized financial system, a subset of the set of one or more candidate products or services, wherein the identifying comprises determining that the respective probability of purchase associated with each candidate product or service of the subset meets or exceeds a threshold value;   identifying, by the computerized financial system, a particular candidate product or service of the subset based at least in part on a respective future product or service value associated with the particular candidate product or service and included in the one or more computational values, wherein the recommended next action comprises an offering of the particular candidate product or service.   
     
     
         43 . The method of  claim 42 , wherein the threshold value is a first threshold value, the subset is a first subset, and the product/service origination optimization process further comprises:
 determining, by the computerized financial system, a respective future product or service value associated with each of at least one candidate product or service of the first subset;   determining, by the computerized financial system, a future customer value associated with the customer based at least in part on the respective future product or service value associated with the each of the at least one candidate product or service of the first subset;   determining, by the computerized financial system, that the future customer value meets or exceeds a second threshold value; and   identifying, by the computerized financial system, a second subset of one or more candidate products or services from the first subset, wherein the respective future product or service value associated with each candidate product or service of the second subset is greater than the respective future product or service value associated with each candidate product or service of the first subset that does not form part of the second subset,   wherein the particular candidate product or service is included in the second subset.   
     
     
         44 . The method of  claim 43 , wherein the product/service origination optimization process further comprises:
 identifying, by the computerized financial system, a third subset of one or more candidate products or services from the second subset, wherein each candidate product or service in the third subset is not currently held by the customer;   identifying, by the computerized financial system, a candidate product or service of the third subset that is associated with a greater respective future product or service value than each other candidate product or service in the third subset; and   selecting, by the computerized financial system, the identified candidate product or service of the third subset as the particular candidate product or service.   
     
     
         45 . The method of  claim 42 , wherein the threshold value is a first threshold value, and wherein identifying the particular candidate product or service of the subset that the respective future product or service value associated with the particular candidate product or service meets or exceeds a second threshold value. 
     
     
         46 . The method of  claim 40 , wherein the particular optimization process comprises the relationship optimization process, and wherein the relationship optimization process comprises:
 determining, by the computerized financial system, a current customer value associated with the customer;   determining, by the computerized financial system, a future customer value associated with the customer;   determining, by the computerized financial system, an overall customer value associated with the customer based at least in part on a combination of the current customer value and the future customer value; and   determining, by the computerized financial system, a value level associated with the customer based at least in part on at least one of: (i) a comparison of the overall value associated with the customer to one or more overall value thresholds or (ii) a comparison of the future customer value to a first threshold and a comparison of the current customer value to a second threshold,   wherein the recommended next action is determined based at least in part on the value level associated with the customer.   
     
     
         47 . The method of  claim 46 , wherein the relationship optimization process further comprises:
 identifying, by the computerized financial system, a respective set of one or more eligibility rules associated with each of one or more candidate recommended next actions;   analyzing, by the computerized financial system, the respective set of one or more eligibility rules associated with each of at least one of the one or more candidate recommended next actions;   determining, by the computerized financial system and based at least in part on the analyzing, that each eligibility rule in the respective set of one or more eligibility rules associated with a particular candidate recommended next action is satisfied; and   selecting, by the computerized financial system, the particular candidate recommended next action as the recommended next action.   
     
     
         48 . The method of  claim 41 , wherein the particular optimization process is a revenue/cost improvement optimization process, wherein the derived data comprises the data indicative of a customer segment with which the customer is associated and the data indicative of one or more predictive model values, wherein the one or more predictive model values comprise an attrition risk associated with the customer, and wherein the revenue/cost improvement optimization process comprises:
 determining, by the computerized financial system, that the attrition risk meets or exceeds a threshold value;   determining, by the computerized financial system, a set of one or more candidate products or services based at least in part on the customer segment;   identifying, by the computerized financial system, a subset of one or more candidate products or services from the set of one or more candidate products or services, wherein the one or more candidate products or services of the subset are not currently held by the customer;   identifying, by the computerized financial system, a respective set of one or more eligibility rules associated with each candidate product or service in the subset;   analyzing, by the computerized financial system, the respective set of one or more eligibility rules associated with each candidate product or service in the subset; and   determining, by the computerized financial system and based at least in part on the analyzing, that each eligibility rule in the respective set of one or more eligibility rules associated with a particular candidate product or service is satisfied,   wherein the recommended next action is an offering of the particular candidate product or service.